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Genome Sequencing Analysis of Blood Cells Identifies Germline

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Genome Sequencing Analysis of Blood Cells Identifies Germline Bhuvaneshwar et al. BMC Cancer (2019) 19:357 https://doi.org/10.1186/s12885-019-5474-y RESEARCHARTICLE Open Access Genome sequencing analysis of blood cells identifies germline haplotypes strongly associated with drug resistance in osteosarcoma patients Krithika Bhuvaneshwar1* , Michael Harris1, Yuriy Gusev1, Subha Madhavan1, Ramaswamy Iyer2, Thierry Vilboux2, John Deeken2, Elizabeth Yang3,4,5,6 and Sadhna Shankar3,4 Abstract Background: Osteosarcoma is the most common malignant bone tumor in children. Survival remains poor among histologically poor responders, and there is a need to identify them at diagnosis to avoid delivering ineffective therapy. Genetic variation contributes to a wide range of response and toxicity related to chemotherapy. The aim of this study is to use sequencing of blood cells to identify germline haplotypes strongly associated with drug resistance in osteosarcoma patients. Methods: We used sequencing data from two patient datasets, from Inova Hospital and the NCI TARGET. We explored the effect of mutation hotspots, in the form of haplotypes, associated with relapse outcome. We then mapped the single nucleotide polymorphisms (SNPs) in these haplotypes to genes and pathways. We also performed a targeted analysis of mutations in Drug Metabolizing Enzymes and Transporter (DMET) genes associated with tumor necrosis and survival. Results: We found intronic and intergenic hotspot regions from 26 genes common to both the TARGET and INOVA datasets significantly associated with relapse outcome. Among significant results were mutations in genes belonging to AKR enzyme family, cell-cell adhesion biological process and the PI3K pathways; as well as variants in SLC22 family associated with both tumor necrosis and overall survival. The SNPs from our results were confirmed using Sanger sequencing. Our results included known as well as novel SNPs and haplotypes in genes associated with drug resistance. Conclusion: We show that combining next generation sequencing data from multiple datasets and defined clinical data can better identify relevant pathway associations and clinically actionable variants, as well as provide insights into drug response mechanisms. Keywords: Whole genome sequencing, Childhood cancers, Drug resistance, Osteosarcoma, Pharmacogenomics, Genetics * Correspondence: [email protected] 1Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington DC, USA Full list of author information is available at the end of the article © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Bhuvaneshwar et al. BMC Cancer (2019) 19:357 Page 2 of 16 Background great variation seen in human health and disease. Germ- Osteosarcoma (OS) is the commonest malignant bone line genetic variation between individuals may lie at the tumor in children. It accounts for about 2% of childhood heart of two critical questions: who is at risk to develop cancers in the US. Approximately 800 new cases are cancer, and how best to treat individuals once they are diagnosed in the US every year, about 400 of which are diagnosed. This genetic variation may account for the in children and teens [1]. OS occurs mostly between the wide variation seen in the response and toxicity related ages of 10 and 30. Approximately 60% of all malignant to chemotherapeutic agents. bone tumors diagnosed in the first two decades of life The pharmacogenetic differences between patients are are osteosarcomas. multi-factorial [12]. One factor is polymorphism in drug Theroleofadjuvantchemotherapy is well established in targets, including cell surface receptors and target proteins. the treatment of osteosarcoma [2, 3]. Prior to use of sys- Another is polymorphism in cellular recovery mechanisms temic chemotherapy, two-year survival was less than 20% that repair cytotoxic agent-induced damage. Finally, there even in patients with clinically localized disease. Most are polymorphisms in genes encoding proteins involved in recent studies report a 3-year survival of 60–70% drug pharmacokinetics, including proteins that impact drug among patients with non-metastatic disease treated absorption, metabolism, distribution, and elimination with combination chemotherapy. Standard treatment for (ADME). Germline pharmacogenetic biomarkers have been non-metastatic osteosarcoma includes neo-adjuvant found for a number of anticancer agents, including irinote- chemotherapy followed by surgical resection and can, mercaptopurine, 5-flurouracil, and tamoxifen [13–16]. post-operative chemotherapy. The extent of necrosis of These studies often used a candidate gene approach, and the primary tumor at time of definitive surgical resection attempted to explain a drug’s efficacy or toxicity by identify- is the only significant prognostic factor in patients with ing one gene and even one variant within that gene [17]. non-metastatic osteosarcoma. Patients with less than 10% While candidate gene/single variant analysis provides viable tumor at time of definitive surgery have significantly important insights, there are several limitations in most lower risk of relapse as compared to those with more than published studies. The implicated alleles were often low 10% viable tumor [4]. Five-year survival is 75–80% among frequency and the absolute numbers of patients with patients with good histological response and only 45–55% these alleles were low. Only one or few single nucleotide among poor responders even after complete surgical polymorphisms (SNPs) were examined for each gene resection [5, 6]. of interest and potentially significant polymorphisms The most active chemotherapeutic agents against could have been missed. In this study, we applied osteosarcoma include cisplatin, doxorubicin and high both single SNP and multiple SNP analysis to get an dose methotrexate. The Children’s Oncology Group enhanced understanding of genetic polymorphism in (COG) considers combination cisplatin, doxorubicin and the disease. high dose methotrexate (MAP) as standard therapy for A genome-wide approach enables examination of poly- osteosarcoma [7]. Postoperative therapy is often modi- morphisms of a large number of implicated genes in fied in poor responders to improve outcome, such as the multiple pathways that may impact on response to use of ifosfamide and etoposide [8–10]. However, no chemotherapy. With advance in technology and reduction such attempts have been successful to date. It is likely in costs, whole-genome sequencing is now feasible with that the initial 8–12 weeks of ineffective therapy select next-generation sequencing. Complete sequences of impli- for resistant clones and allow the cancer cells to cated genes can be analyzed to identify polymorphisms in metastasize. Changing therapy at a later time point fails the context of pathway datasets, rather than as individual to change the outcome. There is a need to identify the data points. An integrative approach combining whole poor responders at time of initial diagnosis to avoid genome sequencing (WGS) with multiple datasets and delivering ineffective pre-operative therapy. Alternative defined clinical data can 1) better capture pathway associ- chemotherapeutic agents at initial diagnosis could ations, and 2) provide the opportunity for discovery of potentially alter outcomes in patients expected to have clinically actionable variants [18, 19]. poor response to standard cisplatin based chemotherapy. We have previously used this integrative approach to Thus, the key challenge is to determine the basis for define the pharmacogenetic profile of gemcitabine, and response and non-response in patients at the outset and defined a ‘sensitive’ and ‘resistant’ genotype using the identify patients who are eligible for intensified or alter- combination of pre-clinical data from the NCI60 cell native therapy given their personal profile. lines and a genome wide association studies (GWAS) The completion of the HapMap project is a historic clinical dataset from a large clinical trial [20]. We now achievement [11]. The project identified over one wish to expand this approach to pediatric osteosarcoma million single nucleotide polymorphisms (SNPs) across patients treated with multi-agent chemotherapy, includ- the human genome, which may be at the root of the ing cisplatin, doxorubicin, and methotrexate. Bhuvaneshwar et al. BMC Cancer (2019) 19:357 Page 3 of 16 Theaimofthisstudyistoidentify,test,andvalidatea week 1; Methotrexate 12 g/m2 on day 1 of week 4, week genotype resistant to cisplatin, doxorubicin, and methotrex- 5. This entire cycle is repeated week 6–10. Surgical re- ate, in children with osteosarcoma, using two datasets section with tumor necrosis data on week 12. derived from clinical samples. The genetic signature that is strongly associated with drug response could be translated Processing of genomic data into clinical oncology [21] and used in the future to We used open source well-known best practices tools in personalize therapy. the processing of sequencing
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